Leaf disease detection using deep Convolutional Neural Networks

Abstract

The automatic recognition of plant diseases is of crucial importance for the current development of agriculture. Fast and efficient identification can greatly reduce the natural, economic, and human resource loss caused to agricultural practitioners. Deep neural networks allow computers to learn plant disease detection in an end-to-end manner, thereby obtaining better results and higher efficiency. While Convolutional Neural Network (CNN) models have become a well-established tool for detecting plant diseases, the lack of robustness of the models due to environmental variations remains to be a critical concern. Recent research into overcoming this challenge includes domain adaptation (DA) algorithms like classic Domain-Adversarial Neural Network (DANN) or the innovative Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). However, the topic remains under-explored as the newly developed methods were not tested on many crop species and diseases. This research focuses on four deep CNN models (MobileNet, VGG, GoogLenet, and ResNet). The models are developed and tested using the New Plant Diseases dataset on Kaggle, which comprises 70,000+ training images (offline-augmented) and 17,000+ validation images encompassing 38 different classes of healthy and diseased plant leaves. The models would be cross-evaluated upon their accuracy and training speed, as well as their change in performance after optimization and applying DA methods. With an uppermost accuracy of 86.4% in test dataset from the wild, results show that Transfer Learning, Model Ensemble as well as Domain Adaptation works effectively to increase the robustness of models which will ultimately benefit farmers in detecting plant diseases and deciding on the best treatment in real-time.